{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入pandas库，用于整理数据，并作建单的图表\n",
    "import pandas as pd\n",
    "\n",
    "#导入matplotlib库\n",
    "import matplotlib\n",
    "\n",
    "#导入pyplot模块\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#识别图表中的中文字符\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # windows可设为SimHei，mac可设为Hiragino Sans GB\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （1）读取CSV格式文件“task_3.1_zhihu_timeline_answer.csv”，将数据命名为zhihu_data_answer，并查看前部数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类型</th>\n",
       "      <th>id</th>\n",
       "      <th>标签</th>\n",
       "      <th>回答id</th>\n",
       "      <th>回答点赞数</th>\n",
       "      <th>回答评论数</th>\n",
       "      <th>回答感谢数</th>\n",
       "      <th>回答观看数</th>\n",
       "      <th>回答创建时间</th>\n",
       "      <th>问题id</th>\n",
       "      <th>问题题目</th>\n",
       "      <th>问题回答数</th>\n",
       "      <th>问题关注数</th>\n",
       "      <th>评论数</th>\n",
       "      <th>创建时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>answer</td>\n",
       "      <td>12_1574824946.178</td>\n",
       "      <td>二次元</td>\n",
       "      <td>734170020</td>\n",
       "      <td>4859</td>\n",
       "      <td>656</td>\n",
       "      <td>564</td>\n",
       "      <td>1120728</td>\n",
       "      <td>1561974232</td>\n",
       "      <td>62972819</td>\n",
       "      <td>你们见过最好看的 coser 长什么样？</td>\n",
       "      <td>1607</td>\n",
       "      <td>30094</td>\n",
       "      <td>30</td>\n",
       "      <td>1501051575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>answer</td>\n",
       "      <td>13_1574824946.45</td>\n",
       "      <td>生活</td>\n",
       "      <td>629465796</td>\n",
       "      <td>46827</td>\n",
       "      <td>1594</td>\n",
       "      <td>12491</td>\n",
       "      <td>4517999</td>\n",
       "      <td>1553236233</td>\n",
       "      <td>63894266</td>\n",
       "      <td>生活中有哪些残忍的真相？</td>\n",
       "      <td>2505</td>\n",
       "      <td>57217</td>\n",
       "      <td>50</td>\n",
       "      <td>1502701040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>answer</td>\n",
       "      <td>14_1574824946.791</td>\n",
       "      <td>高考</td>\n",
       "      <td>344182099</td>\n",
       "      <td>25739</td>\n",
       "      <td>2465</td>\n",
       "      <td>5072</td>\n",
       "      <td>1575771</td>\n",
       "      <td>1521306846</td>\n",
       "      <td>266837866</td>\n",
       "      <td>你们是怎么考上 985 的，每科多少分？</td>\n",
       "      <td>3527</td>\n",
       "      <td>28092</td>\n",
       "      <td>64</td>\n",
       "      <td>1518015871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>answer</td>\n",
       "      <td>15_1574824946.795</td>\n",
       "      <td>网恋</td>\n",
       "      <td>792191898</td>\n",
       "      <td>8948</td>\n",
       "      <td>1938</td>\n",
       "      <td>965</td>\n",
       "      <td>1261527</td>\n",
       "      <td>1566029684</td>\n",
       "      <td>270608371</td>\n",
       "      <td>网恋奔现失败是种什么体验？</td>\n",
       "      <td>5751</td>\n",
       "      <td>28491</td>\n",
       "      <td>122</td>\n",
       "      <td>1522496497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>answer</td>\n",
       "      <td>17_1574824946.818</td>\n",
       "      <td>健康</td>\n",
       "      <td>868188823</td>\n",
       "      <td>884</td>\n",
       "      <td>246</td>\n",
       "      <td>235</td>\n",
       "      <td>329706</td>\n",
       "      <td>1571893264</td>\n",
       "      <td>27128132</td>\n",
       "      <td>如何有效地戒糖？</td>\n",
       "      <td>500</td>\n",
       "      <td>19048</td>\n",
       "      <td>33</td>\n",
       "      <td>1418828372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       类型                 id   标签       回答id  回答点赞数  回答评论数  回答感谢数    回答观看数  \\\n",
       "0  answer  12_1574824946.178  二次元  734170020   4859    656    564  1120728   \n",
       "1  answer   13_1574824946.45   生活  629465796  46827   1594  12491  4517999   \n",
       "2  answer  14_1574824946.791   高考  344182099  25739   2465   5072  1575771   \n",
       "3  answer  15_1574824946.795   网恋  792191898   8948   1938    965  1261527   \n",
       "4  answer  17_1574824946.818   健康  868188823    884    246    235   329706   \n",
       "\n",
       "       回答创建时间       问题id                  问题题目  问题回答数  问题关注数  评论数        创建时间  \n",
       "0  1561974232   62972819  你们见过最好看的 coser 长什么样？   1607  30094   30  1501051575  \n",
       "1  1553236233   63894266          生活中有哪些残忍的真相？   2505  57217   50  1502701040  \n",
       "2  1521306846  266837866  你们是怎么考上 985 的，每科多少分？   3527  28092   64  1518015871  \n",
       "3  1566029684  270608371         网恋奔现失败是种什么体验？   5751  28491  122  1522496497  \n",
       "4  1571893264   27128132              如何有效地戒糖？    500  19048   33  1418828372  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取CSV文件“task_3.1_zhihu_timeline_answer.csv”\n",
    "f = open(\"task_3.1_zhihu_timeline_answer.csv\",encoding=\"utf-8\") \n",
    "zhihu_data_answer = pd.read_csv(f)\n",
    "\n",
    "# 显示知乎数据\n",
    "zhihu_data_answer.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （2）提取“task_3.1_zhihu_timeline_answer.csv”中【回答点赞数】和【回答感谢数】列数据，转成list，制成散点图，观察两者的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取【回答点赞数】一列数据，并转化成list\n",
    "x = zhihu_data_answer[\"回答点赞数\"].tolist()\n",
    "\n",
    "# 提取【回答感谢数】一列数据，并转化成list\n",
    "y = zhihu_data_answer[\"回答感谢数\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用plt，作散点图\n",
    "# plt.scatter（横坐标数值，纵坐标数值，颜色，标记符号）\n",
    "plt.scatter(x,y,c='b',marker='o')\n",
    "\n",
    "# 设置图片标题\n",
    "plt.title(\"'回答点赞数-回答感谢数'散点图\")\n",
    "# 设置图片横轴标签\n",
    "plt.xlabel(\"回答点赞数\")\n",
    "# 设置图片纵轴标签\n",
    "plt.ylabel(\"回答感谢数\")\n",
    "# 设置图片大小\n",
    "plt.rcParams['figure.figsize'] = (25,15)\n",
    "\n",
    "# 保存图片为\n",
    "plt.savefig(\"scatter_1.png\")\n",
    "\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （3）提取“task_3.1_zhihu_timeline_answer.csv”中【问题回答数】和【问题关注数】列数据，制成散点图，观察两者的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取【问题回答数】一列数据，并转化成list\n",
    "m = zhihu_data_answer[\"问题回答数\"].tolist()\n",
    "\n",
    "# 提取【问题关注数】一列数据，并转化成list\n",
    "n = zhihu_data_answer[\"问题关注数\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用plt，作散点图\n",
    "# plt.scatter（横坐标数值，纵坐标数值，颜色，标记符号）\n",
    "plt.scatter(m,n,c='b',marker='o')\n",
    "\n",
    "# 设置图片标题\n",
    "plt.title(\"'问题回答数-问题关注数'散点图\")\n",
    "# 设置图片横轴标签\n",
    "plt.xlabel(\"问题回答数\")\n",
    "# 设置图片纵轴标签\n",
    "plt.ylabel(\"问题关注数\")\n",
    "# 设置图片大小\n",
    "plt.rcParams['figure.figsize'] = (25,15)\n",
    "\n",
    "# 保存图片为\n",
    "plt.savefig(\"scatter_2.png\")\n",
    "\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （4）读取CSV格式文件“task_3.2_zhihu_timeline_article.csv”，将数据命名为zhihu_data_article，并查看前部数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类型</th>\n",
       "      <th>总 id</th>\n",
       "      <th>关键词</th>\n",
       "      <th>文章 id</th>\n",
       "      <th>文章点赞数</th>\n",
       "      <th>文章评论数</th>\n",
       "      <th>文章题目（文章没有感谢数）</th>\n",
       "      <th>文章观看数</th>\n",
       "      <th>文章创建时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>article</td>\n",
       "      <td>14_1574825136.990</td>\n",
       "      <td>李小璐</td>\n",
       "      <td>90620737</td>\n",
       "      <td>774</td>\n",
       "      <td>267</td>\n",
       "      <td>“李小璐出轨视频曝光7天后，我识破了令人胆寒的爱情真相”</td>\n",
       "      <td>245037</td>\n",
       "      <td>1573089756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>article</td>\n",
       "      <td>14_1574825137.717</td>\n",
       "      <td>主持人</td>\n",
       "      <td>90576308</td>\n",
       "      <td>25365</td>\n",
       "      <td>1157</td>\n",
       "      <td>谢娜与董卿同台被“碾压”: 文化断层到底有多可怕?</td>\n",
       "      <td>1072054</td>\n",
       "      <td>1573047546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>article</td>\n",
       "      <td>13_1574825137.707</td>\n",
       "      <td>刘涛</td>\n",
       "      <td>92353372</td>\n",
       "      <td>339</td>\n",
       "      <td>65</td>\n",
       "      <td>刘涛，你够了！</td>\n",
       "      <td>215292</td>\n",
       "      <td>1574051699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>article</td>\n",
       "      <td>14_1574825138.917</td>\n",
       "      <td>网贷风险</td>\n",
       "      <td>83742265</td>\n",
       "      <td>149</td>\n",
       "      <td>90</td>\n",
       "      <td>因面子，深陷网贷的我，最后这样上岸</td>\n",
       "      <td>63565</td>\n",
       "      <td>1569225309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>article</td>\n",
       "      <td>16_1574825138.573</td>\n",
       "      <td>陈情令（电视剧）</td>\n",
       "      <td>93060585</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>你是什么样的含光君？浅析剧版与书版蓝忘机的差异</td>\n",
       "      <td>2779</td>\n",
       "      <td>1574349813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        类型               总 id       关键词     文章 id  文章点赞数  文章评论数  \\\n",
       "0  article  14_1574825136.990       李小璐  90620737    774    267   \n",
       "1  article  14_1574825137.717       主持人  90576308  25365   1157   \n",
       "2  article  13_1574825137.707        刘涛  92353372    339     65   \n",
       "3  article  14_1574825138.917      网贷风险  83742265    149     90   \n",
       "4  article  16_1574825138.573  陈情令（电视剧）  93060585     38      1   \n",
       "\n",
       "                  文章题目（文章没有感谢数）    文章观看数      文章创建时间  \n",
       "0  “李小璐出轨视频曝光7天后，我识破了令人胆寒的爱情真相”   245037  1573089756  \n",
       "1     谢娜与董卿同台被“碾压”: 文化断层到底有多可怕?  1072054  1573047546  \n",
       "2                       刘涛，你够了！   215292  1574051699  \n",
       "3             因面子，深陷网贷的我，最后这样上岸    63565  1569225309  \n",
       "4       你是什么样的含光君？浅析剧版与书版蓝忘机的差异     2779  1574349813  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取CSV格式文件,读取【task_3.2_zhihu_timeline_article.csv】的数据\n",
    "f = open(\"task_3.2_zhihu_timeline_article.csv\",encoding=\"utf-8\") \n",
    "zhihu_data_article = pd.read_csv(f)\n",
    "\n",
    "# 展示数据\n",
    "zhihu_data_article.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （5）提取“task_3.2_zhihu_timeline_article.csv”中【文章点赞数】和【文章评论数】列数据，制成散点图，观察两者的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 提取【文章点赞数】一列数据，并转化成list\n",
    "p =zhihu_data_article[\"文章点赞数\"].tolist()\n",
    "\n",
    "# 提取【文章评论数】一列数据，并转化成list\n",
    "q =zhihu_data_article[\"文章评论数\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用plt，作散点图\n",
    "# plt.scatter（横坐标数值，纵坐标数值，颜色，标记符号）\n",
    "plt.scatter(p,q,c='r',marker='o')\n",
    "\n",
    "# 设置图片标题\n",
    "plt.title(\"'文章点赞数-文章评论数'散点图\")\n",
    "# 设置图片横轴标签\n",
    "plt.xlabel(\"文章点赞数\")\n",
    "# 设置图片纵轴标签\n",
    "plt.ylabel(\"文章评论数\")\n",
    "# 设置图片大小\n",
    "plt.rcParams['figure.figsize'] = (25,15)\n",
    "\n",
    "# 保存图片为\n",
    "plt.savefig(\"scatter_3.png\")\n",
    "\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### （6）提取“task_3.2_zhihu_timeline_article.csv”中【文章点赞数】低于1000的行数据，作出箱型图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类型</th>\n",
       "      <th>总 id</th>\n",
       "      <th>关键词</th>\n",
       "      <th>文章 id</th>\n",
       "      <th>文章点赞数</th>\n",
       "      <th>文章评论数</th>\n",
       "      <th>文章题目（文章没有感谢数）</th>\n",
       "      <th>文章观看数</th>\n",
       "      <th>文章创建时间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>article</td>\n",
       "      <td>14_1574825136.990</td>\n",
       "      <td>李小璐</td>\n",
       "      <td>90620737</td>\n",
       "      <td>774</td>\n",
       "      <td>267</td>\n",
       "      <td>“李小璐出轨视频曝光7天后，我识破了令人胆寒的爱情真相”</td>\n",
       "      <td>245037</td>\n",
       "      <td>1573089756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>article</td>\n",
       "      <td>13_1574825137.707</td>\n",
       "      <td>刘涛</td>\n",
       "      <td>92353372</td>\n",
       "      <td>339</td>\n",
       "      <td>65</td>\n",
       "      <td>刘涛，你够了！</td>\n",
       "      <td>215292</td>\n",
       "      <td>1574051699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>article</td>\n",
       "      <td>14_1574825138.917</td>\n",
       "      <td>网贷风险</td>\n",
       "      <td>83742265</td>\n",
       "      <td>149</td>\n",
       "      <td>90</td>\n",
       "      <td>因面子，深陷网贷的我，最后这样上岸</td>\n",
       "      <td>63565</td>\n",
       "      <td>1569225309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>article</td>\n",
       "      <td>16_1574825138.573</td>\n",
       "      <td>陈情令（电视剧）</td>\n",
       "      <td>93060585</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>你是什么样的含光君？浅析剧版与书版蓝忘机的差异</td>\n",
       "      <td>2779</td>\n",
       "      <td>1574349813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>article</td>\n",
       "      <td>17_1574825138.599</td>\n",
       "      <td>苏州工业园区</td>\n",
       "      <td>79928477</td>\n",
       "      <td>305</td>\n",
       "      <td>200</td>\n",
       "      <td>怎么一夜之间，苏州得到那么多利好</td>\n",
       "      <td>68426</td>\n",
       "      <td>1566872402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>article</td>\n",
       "      <td>16_1574825320.894</td>\n",
       "      <td>恋爱</td>\n",
       "      <td>92878279</td>\n",
       "      <td>417</td>\n",
       "      <td>48</td>\n",
       "      <td>“迷你犬让二哈意外怀孕”爆红韩国！网友：别告诉泰迪！！！</td>\n",
       "      <td>52955</td>\n",
       "      <td>1574285948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>article</td>\n",
       "      <td>14_1574825320.773</td>\n",
       "      <td>体制内</td>\n",
       "      <td>77431584</td>\n",
       "      <td>565</td>\n",
       "      <td>148</td>\n",
       "      <td>在体制内如何正确称呼领导？</td>\n",
       "      <td>135991</td>\n",
       "      <td>1565357442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>article</td>\n",
       "      <td>15_1574825320.715</td>\n",
       "      <td>香港娱乐圈</td>\n",
       "      <td>90663584</td>\n",
       "      <td>380</td>\n",
       "      <td>65</td>\n",
       "      <td>万绮雯：“香港第一美腿”，与甄子丹热恋4年，又7天后闪婚编剧</td>\n",
       "      <td>98046</td>\n",
       "      <td>1573101642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>article</td>\n",
       "      <td>16_1574825320.248</td>\n",
       "      <td>加密/解密</td>\n",
       "      <td>93672329</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>RSA 与SSL</td>\n",
       "      <td>11</td>\n",
       "      <td>1574698008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>article</td>\n",
       "      <td>17_1574825320.876</td>\n",
       "      <td>家居</td>\n",
       "      <td>88551490</td>\n",
       "      <td>134</td>\n",
       "      <td>80</td>\n",
       "      <td>青岛房价重回2016年？</td>\n",
       "      <td>34482</td>\n",
       "      <td>1572019783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          类型               总 id       关键词     文章 id  文章点赞数  文章评论数  \\\n",
       "0    article  14_1574825136.990       李小璐  90620737    774    267   \n",
       "2    article  13_1574825137.707        刘涛  92353372    339     65   \n",
       "3    article  14_1574825138.917      网贷风险  83742265    149     90   \n",
       "4    article  16_1574825138.573  陈情令（电视剧）  93060585     38      1   \n",
       "5    article  17_1574825138.599    苏州工业园区  79928477    305    200   \n",
       "..       ...                ...       ...       ...    ...    ...   \n",
       "489  article  16_1574825320.894        恋爱  92878279    417     48   \n",
       "491  article  14_1574825320.773       体制内  77431584    565    148   \n",
       "492  article  15_1574825320.715     香港娱乐圈  90663584    380     65   \n",
       "493  article  16_1574825320.248     加密/解密  93672329      1      0   \n",
       "495  article  17_1574825320.876        家居  88551490    134     80   \n",
       "\n",
       "                      文章题目（文章没有感谢数）   文章观看数      文章创建时间  \n",
       "0      “李小璐出轨视频曝光7天后，我识破了令人胆寒的爱情真相”  245037  1573089756  \n",
       "2                           刘涛，你够了！  215292  1574051699  \n",
       "3                 因面子，深陷网贷的我，最后这样上岸   63565  1569225309  \n",
       "4           你是什么样的含光君？浅析剧版与书版蓝忘机的差异    2779  1574349813  \n",
       "5                  怎么一夜之间，苏州得到那么多利好   68426  1566872402  \n",
       "..                              ...     ...         ...  \n",
       "489    “迷你犬让二哈意外怀孕”爆红韩国！网友：别告诉泰迪！！！   52955  1574285948  \n",
       "491                   在体制内如何正确称呼领导？  135991  1565357442  \n",
       "492  万绮雯：“香港第一美腿”，与甄子丹热恋4年，又7天后闪婚编剧   98046  1573101642  \n",
       "493                        RSA 与SSL      11  1574698008  \n",
       "495                    青岛房价重回2016年？   34482  1572019783  \n",
       "\n",
       "[305 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取【文章点赞数】低于1000的行数据，并命名为df\n",
    "df = zhihu_data_article[zhihu_data_article[\"文章点赞数\"] < 1000]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[774, 339, 149, 38, 305, 417, 171, 170, 468, 521, 2, 276, 565, 3, 76, 2, 100, 709, 28, 706, 398, 19, 786, 1, 553, 106, 29, 434, 16, 366, 273, 8, 927, 127, 13, 93, 364, 7, 28, 600, 498, 807, 909, 170, 485, 36, 328, 256, 99, 177, 9, 20, 1, 37, 1, 76, 148, 126, 32, 1, 1, 36, 4, 33, 6, 920, 619, 158, 35, 2, 45, 114, 783, 3, 6, 13, 293, 331, 800, 19, 370, 13, 315, 354, 563, 1, 628, 11, 4, 773, 316, 0, 131, 53, 656, 13, 1, 314, 307, 268, 503, 116, 1, 641, 1, 2, 142, 521, 74, 324, 400, 380, 32, 876, 80, 19, 620, 107, 399, 106, 304, 661, 177, 200, 859, 545, 305, 53, 774, 529, 149, 222, 417, 712, 1, 339, 7, 170, 305, 468, 521, 134, 1, 680, 1, 1, 3, 565, 276, 76, 1, 165, 709, 6, 266, 25, 121, 10, 706, 553, 26, 35, 29, 8, 1, 26, 106, 724, 8, 75, 366, 273, 786, 8, 927, 2, 128, 3, 153, 364, 247, 129, 13, 829, 1, 171, 226, 909, 398, 170, 17, 99, 600, 919, 3, 348, 469, 76, 5, 9, 148, 328, 12, 9, 36, 34, 37, 1, 2, 15, 619, 38, 36, 76, 52, 158, 20, 3, 13, 121, 800, 315, 628, 45, 1, 177, 0, 4, 7, 354, 51, 256, 268, 8, 314, 1, 0, 316, 503, 72, 783, 116, 498, 641, 175, 142, 1, 1, 2, 157, 370, 33, 32, 324, 80, 563, 74, 331, 109, 155, 399, 304, 842, 0, 13, 773, 661, 177, 228, 39, 305, 6, 91, 261, 131, 774, 149, 712, 876, 222, 16, 161, 170, 468, 339, 1, 395, 521, 305, 1, 223, 7, 3, 276, 76, 19, 107, 14, 709, 121, 417, 565, 380, 1, 134]\n"
     ]
    }
   ],
   "source": [
    "# 提取【文章点赞数】一列数据\n",
    "df[\"文章点赞数\"]\n",
    "\n",
    "# 将所提取的数据转化成list\n",
    "s = df[\"文章点赞数\"].tolist()\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 调用plt，作箱型图\n",
    "# plt.boxplot(s)\n",
    "plt.boxplot(s)\n",
    "\n",
    "# 设置图片标题\n",
    "plt.title(\"文章点赞数箱型图\")\n",
    "# 设置图片横轴标签\n",
    "plt.xlabel(\"箱型图\")\n",
    "# 设置图片纵轴标签\n",
    "plt.ylabel(\"点赞数\")\n",
    "# 设置图片大小\n",
    "plt.rcParams['figure.figsize'] = (25,15)\n",
    "\n",
    "# 保存图片为\n",
    "plt.savefig(\"box.png\")\n",
    "\n",
    "# 展示图片\n",
    "plt.show()"
   ]
  }
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